151 citations · 181 across the 8 of their papers we have counts for
8 papers
Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test
Alfonso Annarelli, Emiel Slootman, Claudia Filippi
We introduce quantum Monte Carlo (QMC)-trained multi-state machine-learned (ML) force fields for nonadiabatic excited-state dynamics, targeting photochemical processes in which the…
QMCkl: A Kernel Library for Quantum Monte Carlo Applications
Emiel Slootman, Vijay Gopal Chilkuri, Aurelien Delval +16
Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provid…
Experimental demonstration of boson sampling as a hardware accelerator for monte carlo integration
Malaquias Correa Anguita, Teun Roelink, Sara Marzban +3
We present an experimental demonstration of boson sampling as a hardware accelerator for Monte Carlo integration. Our approach leverages importance sampling to factorize an integra…
Optimizing excited states in quantum Monte Carlo: A reassessment of double excitations
Stuart Shepard, Anthony Scemama, Saverio Moroni +1
Quantum Monte Carlo (QMC) methods have proven to be highly accurate for computing excited states, but the choice of optimization strategies for multiple states remains an active to…
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer
Flaviano Della Pia, Benjamin X. Shi, Yasmine S. Al-Hamdani +30
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and mole…
Shifting sands of hardware and software in exascale quantum mechanical simulations
Ravindra Shinde, Claudia Filippi, Anthony Scemama +1
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. Although the transition from petaflops to exascale comp…
Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
Emiel Slootman, Igor Poltavsky, Ravindra Shinde +4
Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces f…
The role of electronic correlation in the Si(100) reconstruction: a quantum Monte Carlo study
Sorcha B. Healy, Claudia Filippi, P. Kratzer +2
Recent low-temperature scanning tunneling experiments have challenged the generally accepted picture of buckled silicon dimers as the ground state reconstruction of the Si(100) sur…